About The Role
The role owns the end-to-end lifecycle of production machine learning models, bridging the gap between experimental data science and high-scale software engineering. The engineer will focus on building robust, scalable prediction systems and real-time inference pipelines that directly power core product features.
This position collaborates closely with backend engineers and product managers to translate complex business requirements into concrete ML problems. The team prioritizes low-latency execution, reproducible model training, and rigorous automated validation.
Key Responsibilities
- Design, train, and deploy supervised and unsupervised machine learning models, specifically targeting recommendation systems and predictive user modeling
- Develop and scale feature engineering pipelines using Python, SQL, and PySpark to process both batch and real-time streaming data
- Build and maintain robust CI/CD pipelines for ML models, integrating automated testing, containerization via Docker, and orchestration via Kubernetes
- Implement model monitoring systems using Prometheus, Grafana, or specialized MLOps tools to track data drift, conceptual drift, and inference latency
- Optimize model inference performance, utilizing quantization, pruning, or specialized runtime engines like ONNX and TensorRT
- Establish software engineering best practices within the ML lifecycle, including comprehensive unit testing, system integration testing, and rigorous code reviews
What We Are Looking For
- 3-6 years of experience as a Machine Learning Engineer or Software Engineer working directly with ML systems in production
- Strong programming skills in Python and deep familiarity with ML libraries such as PyTorch, scikit-learn, and XGBoost
- Hands-on experience with cloud-native ML tooling, specifically AWS SageMaker, MLflow, or Kubeflow for workflow orchestration
- Solid software engineering fundamentals, including experience with Docker, Git, CI/CD pipelines, and writing clean, scalable backend code
- Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related quantitative field
- Bonus: Experience with real-time streaming technologies like Kafka or Flink, or hands-on implementation of LLM/RAG pipelines
